Number of non zero coefficients for each syndrome for the best glmnet model (α = .11 using all features).

<p><i>t</i>: total, <i>p</i>: points, <i>d</i>: distances, <i>ar</i>: areas and an: angles.</p><p>Number of non zero coefficients for each syndrome for the best glmnet model (α = .11 using all features).</p

Pairwise average misclassification error rate for the best <i>glmnet</i> model.

<p>Pairwise average misclassification error rate for the best <i>glmnet</i> model.</p

Simultaneous average misclassification error (AME) per syndrome.

<p>Simultaneous average misclassification error (AME) per syndrome.</p

Illustration of data set.

<p>(a) Example of registered nodes. (b) Distances between coordinate pairs excluding symmetries. Numbers 1 to 48 correspond to landmarks; red: pairwise edges, excluding symmetries; black: Delaunay triangulation. Example of symmetric distances (25, 24) and (23,24).</p

N6-Adenosine Methylation in MiRNAs

<div><p>Methylation of N6-adenosine (m6A) has been observed in many different classes of RNA, but its prevalence in microRNAs (miRNAs) has not yet been studied. Here we show that a knockdown of the m6A demethylase FTO affects the steady-state levels of several miRNAs. Moreover, RNA immunoprecipitation with an anti-m6A-antibody followed by RNA-seq revealed that a significant fraction of miRNAs contains m6A. By motif searches we have discovered consensus sequences discriminating between methylated and unmethylated miRNAs. The epigenetic modification of an epigenetic modifier as described here adds a new layer to the complexity of the posttranscriptional regulation of gene expression.</p></div

Classification and Visualization Based on Derived Image Features: Application to Genetic Syndromes

<div><p>Data transformations prior to analysis may be beneficial in classification tasks. In this article we investigate a set of such transformations on 2D graph-data derived from facial images and their effect on classification accuracy in a high-dimensional setting. These transformations are low-variance in the sense that each involves only a fixed small number of input features. We show that classification accuracy can be improved when penalized regression techniques are employed, as compared to a principal component analysis (PCA) pre-processing step. In our data example classification accuracy improves from 47% to 62% when switching from PCA to penalized regression. A second goal is to visualize the resulting classifiers. We develop importance plots highlighting the influence of coordinates in the original 2D space. Features used for classification are mapped to coordinates in the original images and combined into an importance measure for each pixel. These plots assist in assessing plausibility of classifiers, interpretation of classifiers, and determination of the relative importance of different features.</p></div

Effect of <i>FTO</i> knockdown on the steady state levels of methylated miRNAs.

<p>X-axis, log2 fold-changes (log2fc) of enrichment after imuunopreciptation with an anti-m6A antibody; y-axis, log2 fold-changes of steady state miRNA levels after <i>FTO</i> knockdown. The values of all 239 methylated miRNAs are shown. The red dotted line is the regression line.</p

Importance weighting.

<p>Illustration of the procedure to compute importance for point δ. Contributions of point p₁, area of triangle t₁, distance d₁, and angle a₁ (blue) are weighted according to distance to δ (red). Distances to p₁, centroid c₁, midpoint m₁, vertex v₁ are used for p₁, t₁, d₁, and a₁, respectively.</p

<i>K</i>nockdown of <i>FTO</i> does not significantly change mRNA levels of genes involved in miRNA biogenesis.

<p>The steady-state mRNA levels of <i>DICER</i>, <i>DROSHA</i>, <i>DGCR8</i> and <i>ADAR</i> were analyzed by qRT-PCR in cells treated with scrambled (scr) and <i>FTO-</i>specific siRNAs, respectively. <i>GAPDH</i> was used as a reference gene. The observed changes were not significant. Merged values of mean ± SD from triplicates per assay for the three independent cell lines FTO1C1, FTO2D4 and FTO3C3 are depicted. <i>FTO</i> kd, <i>FTO</i>-specific siRNA treated cells, scr siRNA, scrambled siRNA treated cells.</p

Average misclassification error for values of tuning parameter λ when α = .11.

<p>Average misclassification error for values of tuning parameter λ when α = .11.</p